This task uses an old version of the automated land cover classification algorithm
Please use the new Automated Land Cover Classification (LULC).
|Imagery Examples||Before and after examples|
|Quickstart||Get started with a Python-based quickstart tutorial|
|Task Runtime||Benchmark runtimes for the algorithm|
|Input Options||Required and optional task inputs|
|Outputs||Task outputs and example contents|
|Advanced Options||Additional information for advanced users|
|Known Issues||Issues users should be aware of|
Output image with Vegetation Mask applied
This script gives the example of a vegetation mask with a single tif file as input.
# Quickstart Example producing a single band vegetation mask from a tif file. # First Initialize the Environment from gbdxtools import Interface gbdx = Interface() #Edit the following path to reflect a specific path to an image raster = 's3://gbd-customer-data/CustomerAccount#/PathToImage/' prototask = gbdx.Task("protogenV2RAV", raster=raster) workflow = gbdx.Workflow([ prototask ]) #Edit the following line(s) to reflect specific folder(s) for the output file (example location provided) workflow.savedata(prototask.outputs.data, location="RAV") workflow.execute() print workflow.id print workflow.status
These are the average runtimes for this algorithm. All benchmark tests were run using a standard set of images, based on our most common customer scenarios. Runtime benchmarks apply to the specific algorithm, and don’t represent the runtime of a complete workflow.
|Sensor Name||Total Pixels||Total Area (k2)||Time(secs)||Time/Area k2|
This task will process only WorldView 2 or WorldView 3 multi-spectral imagery (8-band optical and VNIR data sets) that has been atmospherically compensated by the Advanced Image Preprocessor. Supported formats are .TIF, .TIL, .VRT, .HDR.
The following table lists the Vegetation Mask task inputs.
All inputs are required.
|raster||N/A||S3 URL .TIF only||S3 location of input .tif file processed through AOP_Strip_Processor.|
OPTIONAL SETTINGS: Required = False
No additional optional settings for this task exist.
The following table lists the Vegetation Mask task outputs:
|data||Y||This will explain the output file location and provide the output in .TIF format|
|log||N||S3 location where logs are stored|
To link the workflow of one input image into Advanced Image Preprocessor ( AOP_Strip_Processor) into this task, you must use the follow gbdxtools script in python:
#First initalize the environment #AOP strip processor has input values known to complete the Protogen tasks from gbdxtools import Interface gbdx = Interface() #Edit the following path to reflect a specific path to an image data = 's3://gbd-customer-data/CustomerAccount#/PathToImage/' aoptask2 = gbdx.Task('AOP_Strip_Processor', data=data, bands='MS', enable_acomp=True, enable_pansharpen=False, enable_dra=False) # creates acomp'd multispectral image gluetask = gbdx.Task('gdal-cli') # move aoptask output to root where prototask can find it gluetask.inputs.data = aoptask2.outputs.data.value gluetask.inputs.execution_strategy = 'runonce' gluetask.inputs.command = """mv $indir/*/*.tif $outdir/""" prototask = gbdx.Task('protogenV2RAV') prototask.inputs.raster = gluetask.outputs.data.value workflow = gbdx.Workflow([aoptask2, gluetask, prototask]) #Edit the following line(s) to reflect specific folder(s) for the output file (example location provided) workflow.savedata(prototask.outputs.data, location='RAV') workflow.execute() workflow.status
Data Structure for Expected Outputs:
Your processed imagery will be written as binary .TIF image type UINT8x1 and placed in the specified S3 Customer Location (e.g. s3://gbd-customer-data/unique customer id/named directory/).
1) To run the task in a single workflow with Advanced Image Preprocessor, the tif file must first be removed from the AOP folder with the additional python commands listed in the Advanced section.
2) Thin cloud (cloud edges) might be misinterpreted as vegetation.
If you have any questions or issues with this task, please contact email@example.com .